### Dr Yi Ma

### Biography

Dr. Yi Ma is a Reader within the Institute for Communication Systems (ICS, formerly the Centre for Communication Systems Research, CCSR). He has extensive expertise in the areas of Signal Processing, Machine Learning and Information Theory, with their applications in Telecommunications.

### Research

### Research interests

- Machine learning for future physical layer design
- Transceiver optimization for future communication systems such as URLLC.
- Scalable Distributed MIMO technology
- Opportunistic networking and cooperative communications
- Hybrid data fusion and machine learning for mobile localization
- Estimation, detection and synchronization
- Information theory and coding

**Research projects**

- 2017-2021 Artificial intelligence for future communications (Industry fund)
- 2013-2016 RESCUE (FP7 ICT Consortium)
- 2013-2014 LTE Machine-Type Communications: Phase II (Industry fund)
- 2012-2013 LTE Machine-Type Communications: Phase I (Industry fund)
- 2010-2013 WHERE2 (FP7 ICT Consortium)
- 2011-2012 Wi-Fi Indoor Positioning (EPSRC)
- 2010-2013 EXALTED (FP7 ICT Consortium)
- 2007-2010 WHERE (FP7 ICT Consortium)
- 2005-2007 WINNER2 (FP6 ICT Consortium)
- 2004-2005 4MORE (FP6 ICT Consortium)
- 2006-2009 Mobile VCE-Core 4 (EPSRC)

**PhD Position**

I am constantly looking for well self-motivated PhD candidates with excellent background in Physics, Mathematics, Wireless Communications, or Computer Science. Prior to submit your application, please make sure you have met the following University requirements :

- A 1st class BSc degree or MSc with a Distinction (or equivalent to top 10% internationally).
- A good research proposal (if ICS funding support is requested, please clearly indicate why the proposed research should be financially supported by the ICS).
- For international students, it is essential to meet the University's English requirements (IELTS 6.5 or above (overall) with each section of 6.0 or above).

### My teaching

- EEEM017: Fundamentals of Mobile Communications
- EEE3006: Digital Communications
- Personal and tutorial tutor for undergraduate students.
- Year 1 and Year 2 undergraduate examination officer

### Supervision

# Postgraduate research supervision

**Current PhD Students:**

- Songyan Xue: Deep learning for future modem design
- Ang Li: Deep learning for NOMA
- Lifu Liu: Low-cost mmWave solutions
- Jinfei Wang: Ultra-reliable low-latency communications (URLLC)

**Ex-PhD Students:**

- Hongju Liu (04-08): Channel estimation for multicarrier transmissions
- Na Yi (06-09): Cooperative communications
- Yuanyuan Zhang (06-10): Adaptive cooperative relays
- Mohammad Movahhedian (07-10): Frequency synchronization for multiuser multicarrier transmissions.
- Parisa Cheraghi (09-12): Advanced spectrum sensing techniques
- Ziming He (08-12): Advanced mobile positioning and tracking techniques
- Hui Luo (07-11): Cooperative communications for satellite systems
- Zhengwei Lu (09-13): Pilot-assisted fast spectrum sensing techniques
- Jiancao Hou (10-14): Advanced multiuser-MIMO transmitter design
- Chuyi Qian (10-14): Opportunistic relaying protocols
- Erik Yngvesson (13-17): Coexistence of Massive MIMO in unlicensed bands
- Juan Carlos De Luna Ducoing (14-17): Advanced modulations for scalable multiuser MIMO
- Abdullah Alonazi (11-16): Less-calibrated indoor mobile localization
- Guangyi Wang (12-17): Estimation of pilot contaminated channels
- Raouf Yamani (15-17): Low-complexity vector perturbation for MIMO nonlinear precoding

### My publications

### Publications

scheduling approach to enhance the random beamforming (RBF)

with limited feedback in multiple-input ?multiple-output (MIMO)

broadcast channels. Such scheme was shown to obtain the optimal

scaling law of sum rate in the large number of user regime.

However, for a small number of user cases, the system degrees-of freedom cannot be well exploited, and the accuracy of predicting

users? signal-to-interference-plus-noise ratios (SINRs) is also degraded.

Motivated by these, two strategies are proposed to solve

the problems. Specifically, the conventional spatial domain RBF is

first extended to the space?frequency domain. The first strategy

aims to maximize the number of active users based on users?

initially predicted SINRs; the second strategy is to schedule the

maximum number of users, whose preferred beams can coexist

with each others, with more accurate users? SINRs prediction

method. Computer simulations are carried out to examine the proposed

strategies in terms of the sum rate and the number of active

users. It is shown that the first strategy achieves close performance

to the corresponding brute-force search with lower complexity.

Moreover, the second strategy improves the performance by accurately

predicting users? SINRs at the price of relatively increased

complexity and feedback overhead.

The question of how these properties can be harnessed is explored by considering two perspectives: no cooperation and cooperation between users. For the cooperative scenario, a spatial-domain interweave spectrum sharing scheme is introduced that enables opportunistic transmission at a controlled cost to the license holders. The proposed scheme demonstrates three excellent characteristics: that exploitation of the spatial domain allows opportunistic communication in a ?spatial hole,? that spectrum sharing is effectively enabled by inter-tier cooperation, and finally that in this scenario spatial-domain interweave is feasible with a ?small? (as compared to the number of receive antennas at the incumbent) number of transmit antennas. In essence, this opens the possibility of the incumbents? performance to be traded against opportunistic transmission. In the non-cooperative scenario, a spectrum sharing model between a small and large MU-MIMO system is proposed and analysed. The significant service antenna number asymmetry poses unique challenges and opportunities. In the limit of an infinite number of service antennas at one of the access point, the interference and noise power tends to zero and the transmit power can also be scaled back accordingly. These traits seem ideal for use in a spectrum sharing scenario, but in the present case with the coexistence of a conventional MIMO system and with a finite number of service antennas, how will the system behave? The resulting interference scenario is analysed explicitly both in the uplink and downlink, assuming linear receive and transmit equalizers, respectively. Characterization of the mean SINR operating point and required transmit power are presented, and concise transmit power scaling laws are derived. The scaling laws offer insight into how the system behaves with the number of service antennas and system load.

First, a technique that uses real-valued modulation in fully- and over-loaded cases in large MU-MIMO systems, where there are equal or more UTs than service antennas. It is seen that the use of real constellations with a widely linear equaliser benefits from an increased spatial diversity gain over complex constellations with a linear equaliser. Moreover, a likelihood ascent search (LAS) algorithm post-processing stage is applied to further improve the error performance. Computer simulations show remarkable results for large MU-MIMO sizes in uncoded or coded cases.

Second, recognising that real-valued modulation offers poor modulation efficiency, a real-complex hybrid modulation (RCHM) scheme is proposed, where a mix of real- and complex-valued symbols are interleaved in the spatial and temporal domains. It is seen that RCHM combines the merits of real and complex modulations and enables the adjustment of the diversity-multiplexing tradeoff. Through the system outage probability analysis, the optimal ratio of the number real-to-complex symbols, as well as their optimal power allocation, is found for the RCHM pattern. Furthermore, reliability is improved with a small expense in complexity through the use of a successive interference cancellation (SIC) stage. Results are validated through the mathematical analysis of the average bit error rate and through computer simulations considering single and multiple base station scenarios, which show SNR gains over conventional approaches in excess of 5 dB at 1% BLER.

The results suggest that an expense in complexity is not the only way to improve error performance, but near-optimal reliability is also possible using simple techniques through a reduction in the multiplexing gain. Therefore, rather than a two-way complexity vs. performance tradeoff in MU-MIMO detection, a three-way tradeoff may be more appropriate, and is roughly expressed in the following statement:

?Low complexity, high reliability, high multiplexing gain: choose two.?

hybrid modulation (RCHM), is proposed to scale up

multiuser multiple-input multiple-output (MU-MIMO) detection

with particular concern on the use of equal or approximately

equal service antennas and user terminals. By RCHM, we mean

that user terminals transmit their data sequences with a mix of

real and complex modulation symbols interleaved in the spatial

and temporal domain. It is shown, through the system outage

probability, RCHM can combine the merits of real and complex

modulations to achieve the best spatial diversity-multiplexing

trade-off that minimizes the required transmit-power given a

sum-rate. The signal pattern of RCHM is optimized with respect

to the real-to-complex symbol ratio as well as power allocation.

It is also shown that RCHM equips the successive interference

canceling MU-MIMO receiver with near-optimal performances

and fast convergence in Rayleigh fading channels. This result is

validated through our mathematical analysis of the average biterror-

rate as well as extensive computer simulations considering

the case with single or multiple base-stations.

estimation method for generalized MC-CDMA systems in unknown frequency-selective channels utilizing hidden pi-

lots. It is established that CFO is identifiable in the frequency domain by employing cyclic statistics (CS) and linear re-gression (LR) algorithms. We show that the CS-based estimator is capable of mitigating the normalized CFO (NCFO) to a small error value. Then, the LR-based estimator can be employed to offer more accurate estimation by removing the residual quantization error after the CS-based estimator.

for full-duplex (FD) relaying networks is proposed to mitigate

error propagation effects and improve system spectral efficiency.

The idea is to allow the FD relay node to predict the correctly

decoded symbols of each frame, based on the generalized square

deviation method, and discard the erroneously decoded symbols,

resulting in fewer errors being forwarded to the destination node.

Using the capability for simultaneous transmission and reception

at the FD relay node, our proposed strategy can improve the

transmission efficiency without extra cost of signalling overhead.

In addition, targeting on the derived expression for outage probability,

we compare it with half-duplex (HD) relaying case, and

provide the transmission power and relay location optimization

strategy to further enhance system performances. The results

show that our proposed scheme outperforms the classic relaying

protocols, such as cyclic redundancy check based selective

decode-and-forward (S-DF) relaying and threshold based SDF

relaying in terms of outage probability and bit-error-rate.

Moreover, the performances with optimal power allocation are

better than those with equal power allocation, especially when

the FD relay node encounters strong self-interference and/or it

is close to the destination node.

synchronization problem inherent in orthogonal

frequency-division multiple access (OFDMA) uplink

communications, where the carrier frequency offset (CFO)

for each user may be different, and they can be hardly

compensated at the receiver side. Our major contribution

lies in the development of a novel OFDM receiver that

is resilient to unknown random CFO thanks to the use

of a CFO-compensator bank. Specifically, the whole CFO

range is evenly divided into a set of sub-ranges, with

each being supported by a dedicated CFO compensator.

Given the optimization for CFO compensator a NP-hard

problem, a machine deep-learning approach is proposed

to yield a good sub-optimal solution. It is shown that the

proposed receiver is able to offer inter-carrier interference

free performance for OFDMA systems operating at a wide

range of SNRs.

and noncoherent receiver optimization for multiuser single-input

multiple-output (MU-SIMO) communications through unsupervised

deep learning. It is shown that MU-SIMO can be modeled

as a deep neural network with three essential layers, which

include a partially-connected linear layer for joint multiuser

waveform design at the transmitter side, and two nonlinear layers

for the noncoherent signal detection. The proposed approach

demonstrates remarkable MU-SIMO noncoherent communication

performance in Rayleigh fading channels.

a mobile device, with a single RF chain, shares its message

with a set of mobile devices through narrowband mmWave

channel, an analogue-beam splitting approach is proposed

to achieve a good capacity and coverage trade-off. The

proposed approach aims at maximizing the capacity of

the mmWave multicast channel through antenna-element

grouping and adaptive phase shifting, which takes into

account of the inter-beam interference. When receivers are

randomly distributed on a circle centered at the transmitter,

according to the uniform distribution, it is found

that the impact of inter-beam interference on the channel

capacity can be negligibly small, and thus the analoguebeam

splitting approach can be largely simplified in practice.

Computer simulations are carried out to elaborate our

theoretical study and demonstrate considerable advantages

of the proposed analogue-beam splitting approach.

approach is proposed to tackle the multiuser frequency synchronization

problem inherent in orthogonal frequency-division

multiple-access (OFDMA) uplink communications. The key idea

lies in the use of the feed-forward deep neural network (FF-DNN)

for multiuser interference (MUI) cancellation taking advantage

of their strong classification capability. Basically, the proposed

FF-DNN consists of two essential functional layers. One is

called carrier-frequency-offsets (CFOs) classification layer that

is responsible for identifying the users? CFO range, and another

is called MUI-cancellation layer responsible for joint multiuser

detection (MUD) and frequency synchronization. By such means,

the proposed FF-DNN approach showcases remarkable MUIcancellation

performances without the need of multiuser CFO

estimation. In addition, we also exhibit an interesting phenomenon

occurred at the CFO-classification stage, where the

CFO-classification performance get improved exponentially with

the increase of the number of users. This is called multiuser

diversity gain in the CFO-classification stage, which is carefully

studied in this paper.

for multiuser single-input multiple-output (MU-SIMO) coherent

detection are extensively investigated. According to the ways

of utilizing the channel state information at the receiver side

(CSIR), deep learning solutions are divided into two groups.

One group is called equalization and learning, which utilizes the

CSIR for channel equalization and then employ deep learning for

multiuser detection (MUD). The other is called direct learning,

which directly feeds the CSIR, together with the received signal,

into deep neural networks (DNN) to conduct the MUD. It is found

that the direct learning solutions outperform the equalizationand-

learning solutions due to their better exploitation of the

sequence detection gain. On the other hand, the direct learning

solutions are not scalable to the size of SIMO networks, as

current DNN architectures cannot efficiently handle many cochannel

interferences. Motivated by this observation, we propose

a novel direct learning approach, which can combine the merits

of feedforward DNN and parallel interference cancellation. It is

shown that the proposed approach trades off the complexity for

the learning scalability, and the complexity can be managed due

to the parallel network architecture.